A framework of text mining approach for sentiment analysis of news articles using information agents
Keywords and Phrases
Text mining; Balanced Scorecard
"News articles are an important source providing information about the society. The analysis of news articles helps to measure the social importance of many events and give an understanding about current interests. In this research, the analysis of news is used as a means to recommend or suggest the news articles to the users. A sentiment analysis approach for extracting sentiments associated with positive or negative polarities is illustrated in this research project. The sentiment analysis methodology is based on a text mining technique that captures important keywords from the unstructured data such as news, distinguishes the keywords into positive or negative lexicons, ranks those keywords based on the frequency of occurrences, and recommends the news articles as positive or negative to the users. The proposed approach for sentiment analysis is illustrated with experimental results, and their main implications are discussed in this research project. This research also proposes a Balanced Scorecard framework for evaluating the performance of Information Technology (IT) projects. The Balanced Scorecard consists of four perspectives namely financial, user orientation, internal process, and learning and growth. The proposed framework of Balanced Scorecard incorporates four IT-driven performance measurement perspectives for evaluating the software agent"--Abstract, leaf iii.
Business and Information Technology
M.S. in Information Science and Technology
University of Missouri--Rolla
ix, 92 leaves
© 2006 Balasubramanian Guruswamy, All rights reserved.
Thesis - Citation
Text processing (Computer science)
Print OCLC #
Link to Catalog Record
Full-text not available: Request this publication directly from Missouri S&T Library or contact your local library.http://laurel.lso.missouri.edu/record=b5596014~S5
Guruswamy, Balasubramanian, "A framework of text mining approach for sentiment analysis of news articles using information agents" (2006). Masters Theses. 5866.
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